Enhancement of a global lake and reservoir database to aid climate studies and resource monitoring utilizing satellite radar altimetry

Here, we describe the expansion and enhancement of a large (surface area ≥100 km²) lake and reservoir database (1.GREALD). These efforts have also resulted in the spin-off of two additional databases, one containing lakes and reservoirs (2.GREALD, area 10–99 km²), and the other containing ephemeral lakes (3.GELD, area ≥100 km²). These databases are unique sources for projects that utilize satellite radar altimeter data to monitor surface water levels. While 1.GREALD aims to be a complete catalog, 2.GREALD focuses on reservoirs in response to applied sciences programs that monitor water and energy resources. The creation of 3.GELD has climate change objectives as well as water resources and ecosystem conservation applications. The recording of information pertaining to the potential overpasses (waterbody crossings) of the current and archive satellite altimeters is a primary objective as is the need to highlight any form of controlled water level variation. The permanent water databases now contain 6282 entries, half experience some form of anthropogenic influence and ～430 have been identified as potential climatically sensitive terminal lakes. The revised integral surface area distribution is a power law with exponent ?1.016. Statistics reveal that with altimetric repeat visit times of 10-day to monthly, at least 80% of the permanent water bodies (≥10 km²) have been overflown at some period since the 1990s. Current information on water use and reservoir formation date show that the primary use of the reservoir class is hydroelectric power, and that China, Brazil, India, Turkey, and Vietnam dominate the dam building in recent decades.     

Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.     

Neural Emotion Detection via Personal Attributes

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.     

Laser damage resistance of plasma-sprayed alumina and honeycomb skeleton/alumina composite ceramic coatings

Al₂O₃ and honeycomb skeleton-Al₂O₃ composite coatings on Titanium alloy (Ti–6Al–4V) were prepared by atmospheric plasma spraying. A laser ablation experiment on as-sprayed coatings was performed. In this paper, the laser damage resistance, microstructure, phase composition of Al₂O₃ coatings were examined. 3D Dimensional Confocal Microscopy, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Energy Dispersive Spectrometry (EDS) characterized the laser damage morphology, microstructure, phase composition, and element analysis, respectively. The influence of the honeycomb skeleton on the laser ablation damage on as-sprayed coatings was investigated by a comparative analysis of the laser damage morphology with different laser ablation times and gas flow. The results show that the honeycomb skeleton raises thermal conductivity and thermal diffusivity. Moreover, a “tower”-like dendrite was generated during the laser irradiation of the composite coating. The honeycomb skeleton refined the structure, suppressed crack propagation, and reduced the influence of gas flow on cracks. Under the same experimental laser ablation parameters, the laser damage area of the honeycomb skeleton-Al₂O₃ composite coating was smaller than that of the Al₂O₃ coating. It was demonstrated that the laser damage resistance of the honeycomb skeleton-Al₂O₃ composite coating was superior to that of the Al₂O₃ coating.     

Formation of single-crystalline BaTiO3 nanorods from glycolate by tuning the supersaturation conditions

We have studied peculiarities in the formation of single-crystalline barium titanate (BaTiO₃) nanorods from a glycolate-mediated complex via a single-step hydrothermal process under different supersaturation (S_R) conditions. X-ray diffraction (XRD) showed the formation of pure BaTiO₃ with an S_R of above 19. The tetragonality for the BaTiO₃ (c/a) reached 1.013 at S_R = 19–29 and dropped to 1.010 for S_R = 39. According to the transmission electron microscopy (TEM) and XRD analyses, the rod-shaped particles exhibited single crystallinity and crystal growth along the [001] plane. With scanning electron microscopy (SEM), the morphological evolution from a plate-shaped intermediate precursor (S_R = 6–9) to a rod-shaped product with an aspect ratio of 6–9 (S_R = 19–29), and to non-polar material with an irregular structure (S_R = 39), was observed. The negative slope, linear dependence of the particles’ width and length on the supersaturation level in the range S_R = 19–39 was established for the first time. The replacement of the prevailing crystallization mechanism from in-situ topotactic transformation into dissolution-precipitation above S_R = 19 was observed. It was shown that with a simple regulation of the S_R, the structural and morphological characteristics of the obtained BaTiO₃ nanoparticle can be effectively tuned.     

Development and characterisation of microporous biomimetic scaffolds loaded with magnetic nanoparticles as bone repairing material

Fine-tuning of the scaffolds structural features for bone tissue engineering can be an efficient approach to regulate the specific response of the osteoblasts. Here, we loaded magnetic nanoparticles aka superparamagnetic iron oxide nanoparticles (SPIONs) into 3D composite scaffolds based on biological macromolecules (chitosan, collagen, hyaluronic acid) and calcium phosphates for potential applications in bone regeneration, using a biomimetic approach. We assessed the effects of organic (chitosan/collagen/hyaluronic acid) and inorganic (calcium phosphates, SPIONs) phase over the final features of the magnetic scaffolds (MS). Mechanical properties, magnetic susceptibility and biological fluids retention are strongly dependent on the final composition of MS and within the recommended range for application in bone regeneration. The MS architecture/pore size can be made bespoken through changes of the final organic/inorganic ratio. The scaffolds undertake mild degradation as the presence of inorganic components hinders the enzyme catalytic activity. In vitro studies indicated that osteoblasts (SaOS-2) on MS9 had similar cell behaviour activity in comparison with the TCP control. In vivo data showed an evident development of integration and resorption of the MS composites with low inflammation activity. Current findings suggest that the combination of SPIONs into 3D composite scaffolds can be a promising toolkit for bone regeneration.     

谷氨酰胺代谢在原发性肝癌发生发展及治疗中的研究进展

肝癌是我国高发并且危害性极大的恶性肿瘤，其具有高死亡率并起病隐匿等特点。目前，肝癌治疗主要是以靶向药物，手术和放化疗为主，容易出现复发率高、耐药性强等现象。本文以谷氨酰胺代谢为切入点，对目前研究较多的肝癌相关的谷氨酰胺代谢酶及相关基因分别予以综述，以期为肝癌的发病机制及药物治疗提供开放性新思路。     

Silica-based ceramics toward electromagnetic microwave absorption

Silica-based ceramics have been explored extensively as a class of versatile materials for various applications in architecture, catalysis, energy, machinery, and biomedical engineering. Nevertheless, comprehensive information on silica-based ceramic and electromagnetic microwave (EMW) absorption is scarce, although excellent progress has been made in this field. Here, recent progress in the investigation of silica-based ceramics toward EMW absorption is reviewed. We first introduced the basis of ceramics (characteristics, classification, synthetic methods, potential applications). Subsequently, the silica-based ceramics, including Si-based oxides and alloys, SiOC/SiC/Si₃N₄/SiCN-based composite, Ti₃SiC₂ and composite for EMW absorption were systematically summarized. Notably, the fabrication strategies, absorption properties, and mechanisms of silica-based ceramics are described in detail, with a focus on structure and component design. Lastly, the prospects and ongoing challenges of this field in the future are presented. This review is expected to learn from the past and achieve progress toward the future of silica-based ceramic for EMW absorption.     

Effect of crystal structures on nucleation mechanism in hydrothermal synthesis of MZrO3 (M=Ba,Sr, Ca)

Hydrothermal method is widely used in the synthesis of perovskite-type oxides, whereas few studies are reported for the nucleation mechanism, so that the relationship between the crystal structures and reactive activities of reactants and products is still unknown. Herein, the reaction processes are analyzed on the basis of XRD, SEM and Raman characterizations, and the nucleation mechanism is investigated for the hydrothermal synthesis of MZrO₃ (M = Ba, Sr, Ca). We propose that the negative charged cyclic tetramer complexes [Zr₄(OH)₈(OH)₁₆]^8- form in the hydrothermal reaction, which play major roles in the nucleation process. The tetramer complexes continually dehydrate and condensate to form substructural units composed of alkali-earth ions and 6-fold Zr tetramers; substructural units further dehydrate and distort to form perovskite structures. The reactive activation energy increases with the decreasing of M²⁺ (M = Ba, Sr, Ca) ionic radius because the incorporation of smaller A site ions in the perovskite structure is accompanied by greater rotation and distortion of the ZrO₆ octahedra, leading to the decrease of reactive activity accordingly. In a word, the proposed nucleation mechanism in this paper is of great significance for the study of perovskite.     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.